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Latent Class approach to analyze children’s nutritional trajectory and school dropout. A longitudinal population-based application

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  • Alejandra Marroig

    (Universidad de la República)

  • Graciela Muniz-Terrera

    (OHIO University
    University of Edinburgh)

Abstract

The study of the nutritional status is relevant during the entire life course, but in children it is relevant as malnutrition may be a marker of underlying functional and mental health deficits. Evidence of the association between malnutrition and school dropout is not conclusive. Our aim was to analyze children’s nutritional trajectory measured using their Body Mass Index (BMI) of a Uruguayan cohort and its association with school dropout. With this purpose, Latent Class and Joint Latent Class Mixed Models were fitted to children’s cohort study (N = 1392 girls and 1492 boys) in sex-stratified analyses adjusting for sociodemographic characteristics. We identified latent classes of boys and girls with similar BMI trajectories during school years and differences in relevant socioeconomic and anthropometric characteristics. Results indicated that boys dropped out at younger ages than girls. No association between age of school dropout and nutritional trajectory classes was found. None of the classes exhibited a deficit or decrease in BMI trajectories during school ages, although the obesity and overweight classes could be of concern. Results suggested no significant association between obesity or overweight and age of school dropout for children up to 14 years old. Future research on other samples may inform about trajectories in higher educational levels.

Suggested Citation

  • Alejandra Marroig & Graciela Muniz-Terrera, 2023. "Latent Class approach to analyze children’s nutritional trajectory and school dropout. A longitudinal population-based application," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1519-1531, April.
  • Handle: RePEc:spr:qualqt:v:57:y:2023:i:2:d:10.1007_s11135-022-01421-w
    DOI: 10.1007/s11135-022-01421-w
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    References listed on IDEAS

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    1. Marina Bassi & Matias Busso & Juan Sebastian Muñoz, 2015. "Enrollment, Graduation, and Dropout Rates in Latin America: Is the Glass Half Empty or Half Full?," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Fall 2015), pages 113-156, October.
    2. Proust-Lima, Cécile & Joly, Pierre & Dartigues, Jean-François & Jacqmin-Gadda, Hélène, 2009. "Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1142-1154, February.
    3. Failache, Elisa & Salas, Gonzalo & Vigorito, Andrea, 2018. "Desarrollo en la infancia y trayectorias educativas de los adolescentes. Un estudio con base en datos de panel para Uruguay," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(337), pages .81-113, enero-mar.
    4. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    5. Proust-Lima, Cécile & Philipps, Viviane & Liquet, Benoit, 2017. "Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i02).
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